Considering the natural tendency of people to follow direct or indirect cues of other people's activities, collaborative filtering-based recommender systems often predict the utility of an item for a particular user according to previous ratings by other similar users. Consequently, effective searching for the most related neighbors is critical for the success of the recommendations. In recent years, collaborative tagging systems with social bookmarking as their key component from the suite of Web 2.0 technologies allow users to freely bookmark and assign semantic descriptions to various shared resources on the web. While the list of favorite web pages indicates the interests or taste of each user, the assigned tags can further provide useful hints about what a user thinks of the pages.In this paper, we propose a new collaborative filtering approach TBCF (Tag-based Collaborative Filtering) based on the semantic distance among tags assigned by different users to improve the effectiveness of neighbor selection. That is, two users could be considered similar not only if they rated the items similarly, but also if they have similar cognitions over these items. We tested TBCF on real-life datasets, and the experimental results show that our approach has significant improvement against the traditional cosine-based recommendation method while leveraging user input not explicitly targeting the recommendation system.

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Today's Web Portals suffer from information overload. We try to overcome this drawback by making them more adaptable and adaptive to the user's contexts. Therefore, we focus on the utilization of Web 2.0 techniques, especially semantic annotations, to make use of the portal users' collective intelligence.

Web service descriptions with Semantic Web annotations can be exploited to automate dynamic discovery of services. The approaches introduced aim at enabling automatic discovery, configuration, and execution of services in dynamic environments. In this chapter, the authors present the service discovery aspect of MERCURY, a platform for straightforward, user-centric integration and management of heterogeneous devices and services via a Web-based interface. In the context of MERCURY, they use service discovery to find appropriate sensors, services, or actuators to perform a certain functionality required within a user-defined scenario (e.g., to obtain the temperature at a certain location, book a table at a restaurant close to the location of all friends involved, etc.). A user will specify a service request, which will be fed to a matchmaker, which compares the request to existing service offers and ranks these offers based on how well they match the service request. In contrast to existing works, the service discovery approach the authors use is geared towards non-IT-savvy end users and is not restricted to single service-description formalism. Moreover, the matchmaking algorithm should be user-aware and environmentally adaptive (e.g. depending on the user’s location or surrounding temperature), rather than specific to simple keywords-based searches, which depend on the users’ expertise and mostly require several tries. Hence, the goal is to develop a service discovery module on top of existing techniques, which will rank discovered services to serve users’ queries according to their personal interests, expertise, and current situations.